Intense rainfalls prediction models for the state of Mato Grosso, Brazil
Rain intensity data are necessary to increase security of hydraulic projects. The objective of this study was to determine the relationships among intensity-duration-frequency (IDF) and Bell’s model for the State of Mato Grosso, Brazil. The equations were obtained by disaggregation of 24 h rainfall...
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Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHi)
2011-12-01
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doaj-55b427e0887e413282d6a817c6c142092020-11-24T23:31:35ZengInstituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHi)Revista Ambiente & Água1980-993X2011-12-016327429010.4136/ambi-agua.553Intense rainfalls prediction models for the state of Mato Grosso, BrazilSidney PereiraMarcelo Ribeiro ViolaNara Rúbia de MoraisLuiz Fernando CoutinhoRain intensity data are necessary to increase security of hydraulic projects. The objective of this study was to determine the relationships among intensity-duration-frequency (IDF) and Bell’s model for the State of Mato Grosso, Brazil. The equations were obtained by disaggregation of 24 h rainfall data from 136 rain stations available in the National Water Agency (ANA) data base. Employing Gumbel distribution, the rainfalls were estimated for each time duration and for the return periods of 2, 5, 10, 25, 50 and 100 years, and thereafter for each season. The coefficients of IDF relationships and Bell’s models were adjusted by the minimum square method, for all seasons evaluated. The coefficients of determination and Willmott agreement index exceeded 0.98 and 0.85, respectively, for all stations, which classifies the adjustment of the rainfall models as great.http://www.ambi-agua.net/seer/index.php/ambi-agua/article/view/553rainfallBell’s modelintensity-duration-frequency |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sidney Pereira Marcelo Ribeiro Viola Nara Rúbia de Morais Luiz Fernando Coutinho |
spellingShingle |
Sidney Pereira Marcelo Ribeiro Viola Nara Rúbia de Morais Luiz Fernando Coutinho Intense rainfalls prediction models for the state of Mato Grosso, Brazil Revista Ambiente & Água rainfall Bell’s model intensity-duration-frequency |
author_facet |
Sidney Pereira Marcelo Ribeiro Viola Nara Rúbia de Morais Luiz Fernando Coutinho |
author_sort |
Sidney Pereira |
title |
Intense rainfalls prediction models for the state of Mato Grosso, Brazil |
title_short |
Intense rainfalls prediction models for the state of Mato Grosso, Brazil |
title_full |
Intense rainfalls prediction models for the state of Mato Grosso, Brazil |
title_fullStr |
Intense rainfalls prediction models for the state of Mato Grosso, Brazil |
title_full_unstemmed |
Intense rainfalls prediction models for the state of Mato Grosso, Brazil |
title_sort |
intense rainfalls prediction models for the state of mato grosso, brazil |
publisher |
Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHi) |
series |
Revista Ambiente & Água |
issn |
1980-993X |
publishDate |
2011-12-01 |
description |
Rain intensity data are necessary to increase security of hydraulic projects. The objective of this study was to determine the relationships among intensity-duration-frequency (IDF) and Bell’s model for the State of Mato Grosso, Brazil. The equations were obtained by disaggregation of 24 h rainfall data from 136 rain stations available in the National Water Agency (ANA) data base. Employing Gumbel distribution, the rainfalls were estimated for each time duration and for the return periods of 2, 5, 10, 25, 50 and 100 years, and thereafter for each season. The coefficients of IDF relationships and Bell’s models were adjusted by the minimum square method, for all seasons evaluated. The coefficients of determination and Willmott agreement index exceeded 0.98 and 0.85, respectively, for all stations, which classifies the adjustment of the rainfall models as great. |
topic |
rainfall Bell’s model intensity-duration-frequency |
url |
http://www.ambi-agua.net/seer/index.php/ambi-agua/article/view/553 |
work_keys_str_mv |
AT sidneypereira intenserainfallspredictionmodelsforthestateofmatogrossobrazil AT marceloribeiroviola intenserainfallspredictionmodelsforthestateofmatogrossobrazil AT nararubiademorais intenserainfallspredictionmodelsforthestateofmatogrossobrazil AT luizfernandocoutinho intenserainfallspredictionmodelsforthestateofmatogrossobrazil |
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